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Bayesian estimation of the half-normal regression model with deterministic frontier

 

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Opened Access Bayesian estimation of the half-normal regression model with deterministic frontier
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Author: Ortega, Francisco J.
Gavilán Ruiz, José Manuel
Department: Universidad de Sevilla. Departamento de Economía Aplicada I
Date: 2016
Published in: Computational Statistics, 31 (3), 1059-1078.
Document type: Article
Abstract: A regression model with deterministic frontier is considered. This type of model has hardly been studied, partly owing to the difficulty in the application of maximum likelihood estimation since this is a non-regular model. As an alternative, the Bayesian methodology is proposed and analysed. Through the Gibbs algorithm, the inference of the parameters of the model and of the individual efficiencies are relatively straightforward. The results of the simulations indicate that the utilized method performs reasonably well
Cite: Ortega, F.J. y Gavilán Ruiz, J.M. (2016). Bayesian estimation of the half-normal regression model with deterministic frontier. Computational Statistics, 31 (3), 1059-1078.
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Format: PDF

URI: https://hdl.handle.net/11441/70568

DOI: 10.1007/s00180-016-0648-4

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